Interesting section identification device, interesting section identification method, and interesting section identification program

ABSTRACT

An interesting section identification device includes an object detection unit that detect an object included in a frame extracted from a video, a motion feature value extraction unit that calculates a motion feature value of the object, a distinctiveness calculation unit that calculates a degree of distinctiveness in terms of motion of the object with respect to the frame, and an interesting section identification unit that identifies an interesting section in the video with use of respective calculated degrees of distinctiveness of successive frames.

TECHNICAL FIELD

The present invention relates to an interesting section identificationdevice that identifies a section that interests a user in a video.

BACKGROUND ART

In recent years, users store, in PCs and the like, many contentsincluding videos and images that the users shot by digital imageshooting devices such as digital cameras and video cameras. As thenumber of the stored contents increases, there occurs a demand forclassification of the contents, creation of a digest movie of a videothat is one type of the contents, and so on in order for the users toeasily recognize the details of the contents.

According to a conventional digest movie creation method, usersthemselves designate sections to be used for a digest movie from avideo, and piece the designated sections together to create a digestmovie. However, this method imposes great burdens on the users andrequires expert knowledge, and accordingly there is a demand forsimplification and automation in creating a digest movie of a video.

In response to this demand, since one possible digest movie thatinterests users is a video where a dynamic motion occurs, there has beena digest movie creation method of detecting a motion of a person in avideo from which a digest movie is to be created, and extracting asection where the person makes the motion (see Patent Literature 1 forexample).

CITATION LIST Patent Literature

-   [Patent Literature 1] Japanese Patent Application Publication No.    2006-019387

SUMMARY OF INVENTION Technical Problem

However, the above Patent Literature 1 merely provides extraction of asection where a person makes a motion, and does not guarantee extractionof a section that interests users (hereinafter, a section that interestusers is referred to as interesting section). This causes a possibilitythat an appropriate digest movie cannot be created.

The present invention was made in view of the above problem, and aims toprovide an interesting section identification device capable of creatinga digest movie that interests a user.

Solution to Problem

In order to solve the above problem, an interesting sectionidentification device relating to the present invention is aninteresting section identification device that identifies an interestingsection in a video that is estimated to interest a user, the interestingsection identification device comprising: an object detection unitconfigured to detect an object included in a frame extracted from thevideo; a motion feature value extraction unit configured to calculate amotion feature value of the object in the frame; a distinctivenesscalculation unit configured to calculate a degree of distinctiveness interms of motion of the object with respect to the frame, with use of thecalculated motion feature value of the object in the frame; and aninteresting section identification unit configured to identify theinteresting section in the video, with use of the calculated degree ofdistinctiveness.

Also, an interesting section identification method relating to thepresent invention is an interesting section identification method foruse in an interesting section identification device that identifies aninteresting section in a video that is estimated to interest a user, theinteresting section identification method comprising: an objectdetecting step of detecting an object included in a frame extracted fromthe video; a motion feature value extracting step of calculating amotion feature value of the object in the frame; a distinctivenesscalculating step of calculating a degree of distinctiveness in terms ofmotion of the object with respect to the frame, with use of thecalculated motion feature value of the object in the frame; and aninteresting section identifying step of identifying the interestingsection in the video, with use of the calculated degree ofdistinctiveness.

Also, an interesting section identification program relating to thepresent invention is an interesting section identification program forcausing a computer to perform interesting section identificationprocessing of identifying an interesting section in a video that isestimated to interest a user, the interesting section identificationprogram comprising: an object detecting step of detecting an objectincluded in a frame extracted from the video; a motion feature valueextracting step of calculating a motion feature value of the object inthe frame; a distinctiveness calculating step of calculating a degree ofdistinctiveness in terms of motion of the object with respect to theframe, with use of the calculated motion feature value of the object inthe frame; and an interesting section identifying step of identifyingthe interesting section in the video, with use of the calculated degreeof distinctiveness.

Advantageous Effects of Invention

With the above structure, the interesting section identification deviceidentifies an interesting section based on a degree of distinctivenessof motion of an object in an extracted frame. Accordingly, it ispossible to identify, as an interesting section, a section for examplein which motion is the most active among sections in a video. Thisimproves convenience in creating a digest movie that interests a user.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing an example of the functional structureof an interesting section identification device.

FIG. 2 shows an example of frames of a video and an object extractedfrom each of the frames.

FIG. 3 shows an example of an area on which evaluation of motion of anobject is to be made in a frame.

FIG. 4 shows an example of optical flows calculated for the object inthe frame.

FIG. 5 shows an example of pixels used for evaluating motion of theobject in the frame to determine a motion feature value of the object inthe frame.

FIG. 6 shows an example of a motion feature value of an object in eachframe.

FIG. 7 shows an interesting section identification method.

FIG. 8 is a flowchart showing operations performed by the interestingsection identification device.

FIG. 9 is a flowchart showing operations of calculating motion featurevalues.

FIG. 10 is a flowchart showing operations of calculating degrees ofdistinctiveness.

FIG. 11 explains calculation of degrees of distinctiveness of frames.

FIG. 12 is a block diagram showing an example of the functionalstructure of an interesting section identification device relating toEmbodiment 2.

FIG. 13 is a flowchart showing operations performed by the interestingsection identification device relating to Embodiment 2.

FIG. 14 is a flowchart showing operations of distinctiveness calculationperformed by the interesting section identification device relating toEmbodiment 2.

DESCRIPTION OF EMBODIMENTS Expertise Achieved by the Present Inventor

The present inventor considered usage of a motion of an object such asdescribed in the above Patent Literature 1, in order to identify aninteresting section that interests users in a video by a device forcreating a digest movie. However, the present inventor found that thereis a possibility that if an interesting section is identified simplywith use of a motion of an object, even a section including a monotonousmotion such as a motion of just walking is identified as an interestingsection. In the case where the art of the above Patent Literature 1 isused for example, a video where the same operation is repeated isextracted such as a video where a person who is a subject keeps onwalking. This results in creation of a digest movie that bores theusers.

In view of this, the present inventor considered as to which type ofvideo interests the users as a digest movie, and as a result found thata part where an object makes a distinctive motion often interests theusers. The part is for example a part where the object makes a dynamicmotion or a part where the object makes a unique motion.

The following describes, with respect to an interesting sectionidentification device invented by the present inventor with originalityand ingenuity, a method of calculating a degree of distinctiveness, andidentifying an interesting section for creating a digest movie based onthe calculated degree of distinctiveness.

Embodiment 1

The following describes an interesting section identification devicethat is one embodiment of the present invention, with reference to thedrawings.

<Structure>

FIG. 1 is a block diagram showing the functional structure of aninteresting section identification device 100. FIG. 1 also showsperipheral devices of the interesting section identification device 100.

As shown in FIG. 1, the interesting section identification device 100 isconnected with a shooting device 120 and a display device 130. Theinteresting section identification device 100 extracts an interestingsection from a video shot by the shooting device 120, and causes thedisplay device 130 to display the extracted interesting section. In thepresent embodiment, an interesting section is a video having a fixedtime length (three minutes, for example).

The shooting device 120 is, for example, a device having a function ofshooting and recording videos, such as a movie camera and a digitalcamera. The shooting device 120 is connected with the interestingsection identification device 100 via a USB (Universal Serial Bus) cableor the like.

The display device 130 is, for example, a monitor having a function ofdisplaying images, such as a digital TV, an LCD (Liquid CrystalDisplay), and a PDP (Plasma Display Panel). The display device 130 isconnected with the interesting section identification device 100 via aUSB cable or the like.

The following describes the structure of the interesting sectionidentification device 100 relating to the present invention.

As shown in FIG. 1, the interesting section identification device 100includes a video acquisition unit 101, an object detection unit 102, anobject chasing unit 103, an area determination unit 104, a motionfeature value calculation unit 105, a distinctiveness calculation unit106, an interesting section identification unit 107, and an interestingsection extraction unit 108.

The video acquisition unit 101 has a function of acquiring, from theshooting device 120, videos shot by the shooting device 120. The videoacquisition unit 101 is, for example, composed of interface and softwarefor controlling the interface, such as a USB port and a USB driver forconnecting the USB cables.

The object detection unit 102 has a function of detecting, from eachframe of a video, an object such as a person, a person's face, ananimal, and a car.

The object detection unit 102 performs edge detection on each frame, ormoves a search window within the frame to identify the object inside thesearch window using a classifier for identifying objects. As a result, aperson's face included in the frame is detected such as shown in FIG. 2for example. Note that when the classifier is used for detecting aperson's face, the classifier is referred to also as a face learningdictionary. As shown in FIG. 2, the object detection unit 102 performsobject (face) detection to thereby to detect, from the t_(th) frame 201and the t+l_(th) frame 202 of a video, objects 203 and 204,respectively. Although the classifier is described here as for detectingperson's faces, the classifier may be for detecting animals, cars, andso on other than persons. Also, the object detection unit 102 mayinclude a plurality of classifiers each for detecting a different typeof object. Furthermore, the object detection unit 102 may have afunction of assigning, to an object detected from a frame, informationindicating what type of object a classifier used for detecting theobject is to be used for, as metadata.

The object chasing unit 103 chases each object in each frame detected bythe object detection unit 102, and judge which position in a subsequentframe the object is. The object chasing unit 103 has a function ofassigning the same identifier (object ID) to objects in the frames thatare estimated to be the same object as a result of the chasing. Oneexample of object chasing methods is described with reference to FIG. 2.For example, as shown in FIG. 2, areas for the objects 203 and 204 thatare detected from the frames 201 and 202 which are successive frames,respectively, are in roughly the same position in the frames 201 and202. Accordingly, the object chasing unit 103 judges that the objects203 and 204 are the same object, and assigns the same object ID to theobjects 203 and 204. In other words, in the present embodiment, in thecase where areas for objects in frames are close to each other, theseobjects are identified as the same object. In this way, the objectchasing unit 103 chases each object in each frame to identify objectsincluded in frames as the same object.

The area determination unit 104 has a function of determining an area onwhich evaluation of motion of an object is to be made in each frame of avideo. The area from which motion of the object is to be detectedincludes the object to be chased. The following describes areadetermination performed by the area determination unit 104 withreference to FIG. 3.

FIG. 3 shows an area that is determined for an object included in aframe 301. In the present embodiment, the area determination unit 104determines, as an area from which motion of an object is to be detected,an area 302 including an area 302 a and an area 302 b that are shown bydiagonal lines from left to right and diagonal lines from right to left,respectively, in FIG. 3. Specifically, the area 302 a includes an objectthat is estimated to be a person's face detected by the object detectionunit 102, and the area 302 b is estimated to include a body of theperson based on the size of the person's face. The area 302 b isdetermined beforehand to have the direction and the size in accordancewith the direction and the size of the area 302 a. Note that positionsand ranges of areas corresponding between frames may be the same or notdepending on combination of each two successive frames. The shape andrange of an area determined by the area determination unit 104 aredefined in accordance with the type of object detected by the objectdetection unit 102. Accordingly, the area determination unit 104determines the shape and range of the area in accordance with the typeof object detected by the object detection unit 102.

The motion feature value calculation unit 105 has a function ofcalculating a motion feature value of an object in each frame of avideo. The motion feature value calculation unit 105 specifies pixels ofan area in the frame of the video determined by the area determinationunit 104 (see FIG. 4). FIG. 4 shows pixels of the determined area 302.The motion feature value calculation unit 105 calculates an optical flowof each of the specified pixels (see FIG. 5). FIG. 5 shows an example ofonly part of optical flows of the pixels of the area. The optical flowis calculated by the gradient method for example. According to thegradient method, under the hypothesis that “luminance of a point on anobject does not change after movement”, the motion feature valuecalculation unit 105 estimates a position to which a certain pixel in aframe at a time t will move at time t+1, and calculates a vector basedon a move distance of the certain pixel.

Then, the motion feature value calculation unit 105 calculates a motionfeature value of the area determined by the area determination unit 104with use of the calculated optical flows. The following describes one ofmethods of calculating a motion feature value of an area with referenceto FIG. 4.

As shown in FIG. 4, in the area 302, the coordinate (x,y) of a pixel onthe extreme left on the first line is (a,b), the coordinate (x,y) of apixel on the second from the left on the first line is (a+1,b), . . . ,the coordinate (x,y) of a pixel on the extreme right on the first lineis (a+w,b), the coordinate (x,y) of a pixel on the extreme left on thesecond line is (a,b+1), . . . . The optical flow of the coordinate (a,b)is calculated as (x_(a),y_(b)), the optical flow of the coordinate(a+1,b) is calculated as (x_(a+1),y_(b)), . . . , the optical flow ofthe coordinate (a+w,b) is calculated as (x_(a+w),y_(b)), the opticalflow of the coordinate (a,b+1) is calculated as (x_(a),y_(b+1)), . . . .Then, a motion feature value of the area 302 (feature value 1, featurevalue 2, feature value 3, . . . , feature value K, . . . ) is calculatedas (x_(a),y_(b), x_(a+1),y_(b), . . . , x_(a+w),y_(b), x_(a),y_(b+1), .. . ). In other words, the motion feature value of the area 302, whichis determined by the area determination unit 104, is a set of opticalflows of the pixels of the area 302. Specifically, the set of opticalflows is composed of optical flows of pixels arranged from the extremeleft to the extreme right on the first line, optical flows of pixelsarranged from the extreme left to the extreme right on the second line,. . . , optical flows of pixels arranged from the extreme left to theextreme right on the N_(th) line, . . . , optical flows of pixelsarranged from the extreme left to the extreme right on the lowest line.In this way, the motion feature value calculation unit 105 calculates amotion feature value of each object detected from each frame, and storesinformation indicating the calculated motion feature value in a memoryor the like which is not illustrated.

FIG. 6 is a data conceptual diagram showing information indicatingstored motion feature values. As shown in FIG. 6, the informationindicating the motion feature values shows, for each type of featurevalue, numerical values of feature values and frame numbers each foridentifying a frame in one-to-one correspondence. Here, the type offeature value indicates a motion amount of a motion vector of any pixelof an area in the x-axis direction or the y-axis direction. Theinformation shows, with respect to a frame having a frame number 3, amotion feature value that is composed of a numerical value of six as thefeature value 1 and a numerical value of two as the feature value 2.

The distinctiveness calculation unit 106 has a function of calculating adegree of distinctiveness of each frame of a video. The degree ofdistinctiveness of each frame is an index indicating how much motion ofan object in the frame differs from motion of the object in other frame.The method of calculating degrees of distinctiveness is described indetail later.

The interesting section identification unit 107 has a function ofidentifying an interesting section with use of a degree ofdistinctiveness of each frame calculated by the distinctivenesscalculation unit 106. The following describes a method of identifying aninteresting section with reference to FIG. 7. FIG. 7 is a graph showingthat a degree of distinctiveness varies over time of a video, with thetime on the abscissa and the degree of distinctiveness on the ordinate.The interesting section identification unit 107 shifts a window 701having a fixed predetermined length from the beginning to the end of thevideo, and sums up respective degrees of distinctiveness of framesincluded in each section that is equivalent in length to the window 701.The interesting section identification unit 107 identifies, as aninteresting section, a section where the sum of respective degrees ofdistinctiveness of frames included therein is the highest among thesections that are each equivalent in length to the window 701. Then, theinteresting section identification unit 107 assigns, to the video,information indicating the start point and the end point of theidentified interesting section. Specifically, the interesting sectionidentification unit 107 indexes, to the video, a tag indicating thestart point of the interesting section and a tag indicating the endpoint of the interesting section (hereinafter, referred to as a startpoint tag and an end point tag, respectively).

The interesting section extraction unit 108 has a function ofextracting, from the video, a movie of the interesting section that hasa start point and an end point indicated by the start point tag and theend point tag indexed to the video by the interesting sectionidentification unit 107, respectively.

The output unit 109 has a function of outputting, to the display device130, the movie of the interesting section extracted by the interestingsection extraction unit 108.

The display device 130 plays back the movie of the interesting sectionoutput by the output unit 109. By viewing the movie of the interestingsection, the user can recognize the details of the video for a shortperiod while viewing the movie of the interesting section with interest.

<Operations>

The following describes the operations performed by the interestingsection identification device 100 relating to the present embodiment,with reference to the flowchart in FIG. 8.

Firstly, description is given on basic operations of interesting sectionidentification.

The video acquisition unit 101 included in the interesting sectionidentification device 100 acquires a video input by the shooting device120 via the USB cable (Step S801). The video acquisition unit 101transmits the acquired video to the object detection unit 102.

The object detection unit 102 detects one or more objects included ineach of frames of the video (Step S802). The object detection unit 102transmits information of each of the detected objects to the objectchasing unit 103.

The object chasing unit 103 chases, for each of the objects detectedfrom each of the frames, in which position the object exists in whichframe, and identifies an object that is the same between frames byassigning the same object ID to the object (Step S803).

As a result of the chasing performed by the object chasing unit 103, thearea determination unit 104 determines an area in each frame from whichmotion of the object is to be detected (Step S804).

With respect to the area for each object determined by the areadetermination unit 104, the motion feature value calculation unit 105calculates a motion feature value based on how much the area movesbetween the frame and a subsequent frame (Step S805). In other words,the motion feature value calculation unit 105 calculates, as the motionfeature value of the frame, a vector that is a set of optical flows ofpixels of the area in the frame.

The distinctiveness calculation unit 106 calculates a degree ofdistinctiveness of the frame with use of the motion feature value of theframe (Step S806). Operations of calculating degrees of distinctivenessof frames are described in detail later with reference to a flowchart inFIG. 10.

Based on the calculated degree of distinctiveness of each frame, theinteresting section identification unit 107 identifies, as aninteresting section of the video, a section having the predeterminedlength for interesting section where the sum of respective degrees ofdistinctiveness of frames included therein is the highest among sectionseach having the predetermined length in the video. The interestingsection identification unit 107 indexes information indicating the startpoint and the end point of the interesting section to the video (StepS807).

The interesting section extraction unit 108 extracts a movie of theinteresting section identified by the interesting section identificationunit 107 from the video, and outputs the extracted movie of theinteresting section to the output unit 109. Then, the output unit 109outputs the movie of the interesting section to the display device 130(Step S808). The display device 130 displays the movie of theinteresting section output by the interesting section identificationdevice 100. The user can recognize the details of the video by onlychecking the movie of the interesting section with no need to view theentire video.

The following describes in detail operations of calculating motionfeature values in Step S805 in FIG. 8.

FIG. 9 is a flowchart showing operations of calculating motion featurevalues performed by the motion feature value calculation unit 105.

The motion feature value calculation unit 105 calculates an optical flowof each of pixels of the area 302 in each frame (Step S901).

The motion feature value calculation unit 105 normalizes the calculatedoptical flows in each frame based on the size of an object included inthe frame (Step S902). Based on size information of a person's face thatis the object, the motion feature value calculation unit 105 for exampleincreases the optical flow of each pixel in size by performing linearinterpolation, and decreases the optical flow of each pixel in size byaveraging the sizes of optical flows of the pixels of the area beforedecrease in size. This is because of the following. The size of anobject in a video varies depending on the distance between the shootingdevice 120 and the object that is a subject. In the processing ofdetermining a motion feature value of an object, the size of the objectis defined as constant. Therefore, the normalization is performed.

Then, the motion feature value calculation unit 105 calculates a motionfeature value represented by a set of optical flows of pixels of an areadetermined for an object included in each frame (Step S903).

As a result, a motion feature value of each frame is calculated as shownin FIG. 6.

The following describes in detail operations of calculating degrees ofdistinctiveness with reference to FIG. 10 and FIG. 11.

FIG. 10 is a flowchart showing operations of calculating degrees ofdistinctiveness performed by the distinctiveness calculation unit 106.FIG. 11 shows frames for use in calculating a degree of distinctiveness.FIG. 11 is a three-dimensional graph with the time on the x-axis, thetype of motion feature value on the y-axis, and the feature value on thez-axis. FIG. 11 shows the concept of a motion feature value of a certainobject in each frame of a video in the time axis direction.

Description is given below on a motion feature value for use incalculating a degree of distinctiveness with reference to FIG. 11. Adegree of distinctiveness of a target frame indicates how less a motionfeature value of the frame is similar to respective motion featurevalues of frames anterior and posterior to the target frame that fallwithin a predetermined range. As shown in FIG. 11, a section thatincludes a target frame, M frames anterior to the target frame, and Mframes posterior to the target frame is defined as a first section,where M is an integer equal to or greater than two. Also, a section thatincludes the target frame, N frames anterior to the target frame, and Nframes posterior to the target frame is defined as a second section,where N is an integer equal to or greater than one and less than M. Adegree of distinctiveness of the target frame is calculated with use ofa motion feature value of the target frame and the average of respectivemotion feature values of frames included in a third section. The thirdsection results from subtracting the second section from the firstsection. Note, M frames are 300 frames corresponding to ten seconds ofthe video, and N frames are 60 frames corresponding to two seconds ofthe video.

The further detailed description is given below with reference to theflowchart in FIG. 10.

The distinctiveness calculation unit 106 determines a target frame whosedegree of distinctiveness is to be calculated (Step S1001). It is onlynecessary to calculate a degree of distinctiveness of each of all theframes of the video. Accordingly, the target frame may be determined inorder from the beginning frame to the end frame of the video, determinedin the reverse order from the end frame to the beginning frame of thevideo, or determined at random. In the operations, distinctivenesscalculation starts with the beginning frame of the video.

The distinctiveness calculation unit 106 selects an object included in atarget frame (Step S1002).

Next, the distinctiveness calculation unit 106 calculates an averagemotion feature value that is the average of respective motion featurevalues of (2M−2N) frames included in a third section based on the targetframe including the selected object (Step S1003).

Specifically, a motion feature value of an object A in a target frame kis represented by the following Equation 1.

{right arrow over (a)} _(k)  [Equation 1]

An average motion feature value of the object A in the frames includedin the third section is represented by the following Equation 2. Theframes included in the third section are used for comparison in degreeof distinctiveness with the target frame k.

{right arrow over (a)} _(k,ave)  [Equation 2]

The average motion feature value of the object A in the frames includedin the third section is represented by the following Equation 3.

$\begin{matrix}{{\overset{\rightarrow}{a}}_{k,{ave}} = \frac{{\sum\limits_{i = 1}^{M - N}\; {\overset{\rightarrow}{a}}_{k - {({N + i})}}} + {\sum\limits_{i = 1}^{M - N}\; {\overset{\rightarrow}{a}}_{k + {({N + i})}}}}{2\left( {M - N} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Note that when k≦N is satisfied, the average motion feature value of theobject A in the frames included in the third section, which is used forcalculating the degree of distinctiveness of the target frame k, isrepresented by the following Equation 4.

$\begin{matrix}{{\overset{\rightarrow}{a}}_{k,{ave}} = \frac{\sum\limits_{i = 1}^{M - N}\; {\overset{\rightarrow}{a}}_{k + {({N + i})}}}{M - N}} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack\end{matrix}$

Also, when f_(max)−N≦k is satisfied where f_(max) is the number of allthe frames of the video, the average motion feature value of the objectA relating to the target frame k is represented by the followingEquation 5.

$\begin{matrix}{{\overset{\rightarrow}{a}}_{k,{ave}} = \frac{\sum\limits_{i = 1}^{M - N}\; {\overset{\rightarrow}{a}}_{k - {({N + i})}}}{M - N}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

The distinctiveness calculation unit 106 calculates the score (degree ofdistinctiveness) of the selected object in the target frame k with useof the above Equation 1 and the average motion feature value (StepS1004).

The score of the object A in the target frame k is represented by thefollowing Equation 6.

$\begin{matrix}{P_{k} = \frac{{\overset{\rightarrow}{a}}_{k}}{1 + {{\overset{\rightarrow}{a}}_{k} \cdot {\overset{\rightarrow}{a}}_{k,{ave}}}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack\end{matrix}$

Specifically, the distinctiveness calculation unit 106 adds one to aninner product of the motion feature value of the object A in the targetframe k and the average motion feature value of the object A in theframes included in the third section based on the target frame k. Then,the distinctiveness calculation unit 106 divides the absolute value ofthe motion feature value of the object A in the target frame k by thesum of the addition, and determines a quotient resulting from thedivision as the score of the object A in the target frame k.

The distinctiveness calculation unit 106 judges whether scorecalculation is complete with respect to all the objects included in theselected target frame (Step S1005). When score calculation is notcomplete with respect to all the objects (Step S1005: NO), thedistinctiveness calculation unit 106 returns to Step S1002.

When score calculation is complete with respect to all the objects (StepS1005: YES), the distinctiveness calculation unit 106 sums up therespective calculated degrees of distinctiveness of all the objects toobtain the degree of distinctiveness of the target frame (Step S1006).

Next, the distinctiveness calculation unit 106 judges whetherdistinctiveness calculation is complete with respect to all the frames(Step S1007). When distinctiveness calculation is not complete withrespect to all the frames (Step S1007: NO), the distinctivenesscalculation unit 106 returns to Step S1001.

When distinctiveness calculation is complete with respect to all theframes (Step S1007: YES), the distinctiveness calculation unit 106 endsthe distinctiveness calculation processing.

The interesting section identification device 100 performs theoperations in this way.

With the above structure, the interesting section identification device100 identifies, as an interesting section, a part of a video where amotion of an object particularly varies, thereby providing the user withan interesting section that keeps the user from getting bored.

Embodiment 2

In Embodiment 1, a degree of distinctiveness of a motion of an object ina frame is calculated by comparing with a motion of the object in otherframe. The method of calculating the degree of distinctiveness of theobject is not limited to this. In Embodiment 2, description is given ona method of calculating the degree of distinctiveness different fromthat in Embodiment 1. In Embodiment 2, description of the structuresthat are the same as those in Embodiment 1 is omitted, and differencefrom Embodiment 1 is described.

Also in Embodiment 2, description is given based on the premise that aplurality of objects are included in a video.

<Structure>

An interesting section identification device 1200 relating to Embodiment2 includes, as shown in FIG. 12, a video acquisition unit 101, an objectdetection unit 102, a motion feature value calculation unit 105, adistinctiveness calculation unit 1206, an interesting sectionidentification unit 1207, an interesting section extraction unit 108,and an output unit 109. The functional elements shown in FIG. 12 havingthe same functions as those included in the interesting sectionidentification device 100 shown in FIG. 2 have the same names andreference numerals. Accordingly, description thereof is simplified oromitted.

Information of an object detected by the object detection unit 102 istransmitted to the motion feature value calculation unit 105 via theobject chasing unit 103 and the area determination unit 104.

With respect to each frame of a video, the motion feature valuecalculation unit 105 calculates a motion feature value of each ofobjects included in the frame detected by the object detection unit 102,and transmits the calculated motion feature value to the distinctivenesscalculation unit 1206.

The reception unit 1205 has a function of receiving designation of acertain object selected among the objects detected by the objectdetection unit 102, and transmitting information of the certain objectto the distinctiveness calculation unit 1206.

The distinctiveness calculation unit 1206 has a function of calculatinghow much the certain object is distinctive from other object included inthe same frame.

The distinctiveness calculation unit 1206 selects, among the objectsdetected by the object detection unit 102, the certain object whosedesignation is received by the reception unit 1205. The distinctivenesscalculation unit 1206 calculates how much a motion feature value of theselected certain object is distinctive from a motion feature value ofthe other object.

The interesting section identification unit 1207 has a function ofidentifying an interesting section based on a degree of distinctivenessof a certain object in each frame calculated by the distinctivenesscalculation unit 1206. Specifically, the interesting sectionidentification unit 1207 identifies, as an interesting section, asection having a predetermined length where the sum of respectivedegrees of distinctiveness P of frames included therein calculated bythe distinctiveness calculation unit 1206 is the highest among sectionseach having the predetermined length in the video. The predeterminedlength is the user's desired length for interesting section, and is forexample three minutes or the number of frames corresponding to threeminutes.

<Operations>

The following describes operations of identifying an interesting sectionperformed by the interesting section identification device 1200 relatingto Embodiment 2, with reference to flowcharts in FIG. 13 and FIG. 14. Inthe flowcharts in FIG. 13 and FIG. 14, operations performed by theinteresting section identification device 1200 that are the same asthose by the interesting section identification device 100 relating toEmbodiment 1 shown in flowcharts in FIG. 8 and FIG. 10 have the samereference numerals. Accordingly, description thereof is omitted.

The motion feature value calculation unit 105 included in theinteresting section identification device 1200 calculates a motionfeature value of each object in each frame of a video (Step S805).

Then, the distinctiveness calculation unit 1206 included in theinteresting section identification device 1200 receives the motionfeature value of the object in the frame, and calculates a degree ofdistinctiveness of the object.

The details of a method of calculating a degree of distinctiveness of anobject is as shown in the flowchart in FIG. 14.

After a target frame is determined, the reception unit 1205 receives,from the user, designation of a certain object whose degree ofdistinctiveness is to be calculated (Step S1401).

The distinctiveness calculation unit 1206 calculates the degree ofdistinctiveness of the certain object in the target frame (Step S1404).Specifically, the distinctiveness calculation unit 1206 calculates thedegree of distinctiveness of the designated certain object, with use ofthe average of respective motion feature values of other objectsincluded in the target frame. Specifically, the degree ofdistinctiveness of the certain object is calculated as follows.

Firstly, a motion feature value of a certain object in a target framewhose degree of distinctiveness is to be calculated is represented bythe following Equation 7.

{right arrow over (a)}  [Equation 7]

Also, a motion feature value of each of one or more objects other thanthe certain object in the target frame is represented by the followingEquation 8.

{right arrow over (a)} _(k)  [Equation 8]

Note that k is an index for identifying the other object. Here, theindex ranges from one to n in the target frame.

An average vector b_(k,ave) of respective motion feature values of theother objects is represented by the following Equation 9.

$\begin{matrix}{{\overset{\rightarrow}{b}}_{k,{ave}} = \frac{\sum\limits_{k = 1}^{n}\; {\overset{\rightarrow}{b}}_{k}}{n}} & \left\lbrack {{Equation}\mspace{14mu} 9} \right\rbrack\end{matrix}$

A degree of distinctiveness P of the certain object in the target frameis represented by the following Equation 10.

$\begin{matrix}{P = \frac{\overset{\rightarrow}{a}}{1 + {\overset{\rightarrow}{a} \cdot {\overset{\rightarrow}{b}}_{k,{ave}}}}} & \left\lbrack {{Equation}\mspace{14mu} 10} \right\rbrack\end{matrix}$

The distinctiveness calculation unit 1206 calculates the degree ofdistinctiveness P as the degree of distinctiveness of the target frame(Step S1406).

In this way, the distinctiveness calculation unit 1206 calculates thedegree of distinctiveness of each frame.

The interesting section identification unit 1207 receives the degree ofdistinctiveness of each frame from the distinctiveness calculation unit1206. Then, the interesting section identification unit 1207 identifies,as an interesting section, a section having the predetermined length(equivalent in length to the window 701 shown in FIG. 7) where the sumof respective degrees of distinctiveness of frames included therein isthe highest among sections each having the predetermined length in thevideo.

With this structure, in the case where a video includes a scene ofgymnastic formation in an athletic meet for example, the interestingsection identification device 1200 is useful in extracting an NG scenewhere only one student moves differently from other students. In otherwords, the interesting section identification device 1200 can identify,as an interesting section, a scene where while a plurality of objectsmake the same motion, only one object makes a different motion.

Modification Examples

Although the interesting section identification device relating to thepresent invention has been described based on the above embodiments,embodiments of the present invention are not limited to the aboveembodiments. The following describes modification examples that areincluded as the concept of the present invention.

(1) In the above embodiments, an interesting section identified by theinteresting section identification device 100 (1200) has thepredetermined length. Alternatively, the interesting section does notnecessarily need to have a fixed length.

For example, the interesting section may have a length that accounts fora predetermined rate (for example 10%) of a length of a video from whichthe interesting section is to be extracted.

Further alternatively, the interesting section identification device 100(1200) may change a length of an interesting section to be extracted, inaccordance with the type of video. The type of video indicates the genreof video such as variety, sports, drama, and news. In this case, theinteresting section identification device 100 (1200) stores therein atable with respect to the predetermined length for interesting sectionin which a length of an interesting section is determined incorrespondence with the type of a video. The video acquisition unit 101identifies the type of a video with use of acquired metadata of thevideo or the like. The interesting section identification unit 107(1207) acquires a predetermined length for interesting sectioncorresponding to the identified type of the video to identify aninteresting section having the corresponding length. With thisstructure, the interesting section identification device 100 (1200)identifies an appropriate length of an interesting section in accordancewith the type of a video, thereby identifying a more appropriateinteresting section. In the case where it is impossible to identify thegenre of a video, a user may input the genre of the video to identify alength of an interesting section, or the interesting section of thevideo may be set to have a predetermined length.

Yet alternatively, the interesting section identification device 100(1200) may include a setting unit (interface) for enabling the user toset a predetermined length for interesting section. In this case, theinteresting section identification unit identifies an interestingsection having a predetermined length for interesting section set by theuser. The predetermined length for interesting section may be set by theuser inputting the number of seconds for the interesting section.Alternatively, the predetermined length may be set by the user making aninput on an interface including buttons for setting the predeterminedlength to long, normal, short, and so on. In this way, an interestingsection having the user's desired length may be identified.

(2) In the above embodiments, a first section and a second section,which are used for calculating a degree of distinctiveness of eachframe, each have a predetermined length. Alternatively, the firstsection and the second section each do not necessarily need to have afixed length.

For example, the interesting section identification device 100 (1200)may determine the respective lengths of the first section and the secondsection, in accordance with the type of a video. The type of videoindicates the genre of video such as variety, sports, drama, and news.In this case, the interesting section identification device 100 (1200)stores therein a table with respect to the respective lengths for firstsection and second section in which respective lengths of a firstsection and a second section are determined in correspondence with thetype of video. The video acquisition unit 101 identifies the type of avideo with use of acquired metadata of the video or the like. Thedistinctiveness calculation unit 106 (1206) acquires respective lengthsfor first section and second section corresponding to the type of thevideo to calculate a degree of distinctiveness. With this structure, theinteresting section identification device 100 (1200) identifiesrespective appropriate lengths of a first section and a second sectionin accordance with the type of a video, thereby calculating a degree ofdistinctiveness with a higher precision. In the case where it isimpossible to identify the genre of the video, a user may input thegenre of the video to identify respective lengths of the first sectionand the second section, or the first section and the second section maybe set to each have a predetermined length.

Yet alternatively, the interesting section identification device 100(1200) may include a setting unit (interface) for enabling the user toset respective lengths of the first section and the second section. Inthis case, the distinctiveness calculation unit 106 (1206) calculates adegree of distinctiveness with use of the first section and the secondsection having the predetermined length for first section and thepredetermined length for second section set by the user, respectively.

(3) The distinctiveness calculation method used by the interestingsection identification device 100 (1200) described in the aboveembodiments is just one example. Any other method may be used fordistinctiveness calculation as long as it is possible to calculate howmuch motion of an object in a video is distinctive.

For example, in Embodiment 1, motion feature values of frames includedin a second section are not used for calculating a degree ofdistinctiveness of a target frame. Alternatively, it may be possible toemploy the structure in which the second section is set to have a lengthof zero, and motion feature values of all the frames included in thefirst section other than the target frame are used for comparison tocalculate the degree of distinctiveness of the target frame.

Further alternatively, the numerator in each of the above Equations 6and 10 may be set to one.

Yet alternatively, weighting may be made on respective calculateddegrees of distinctiveness of frames such that a motion feature value ofa certain frame included in the frames is reflected highly among motionfeature values of the frames used for calculating the degrees ofdistinctiveness.

Further alternatively, any method other than the method of modifying theabove Equations 6 and 10 may be employed. For example, an inner productof the motion feature value of the target frame and the average motionfeature value of frames included in the third section may be used withno modification as a degree of distinctiveness of the target frame. Thecloser to zero the inner product is, the higher the degree ofdistinctiveness is. The closer to one the inner product is, the lowerthe degree of distinctiveness is.

(4) In the above embodiments, in the case where a plurality of objectsare detected from a video, the interesting section identification device100 (1200) sums up respective degrees of distinctiveness of the objectsin a target frame, with use of respective motion feature values of theobjects in the target frame, thereby to obtain a degree ofdistinctiveness of the target frame. Alternatively, other method may beuse for calculating the degree of distinctiveness of the target frame.

Specifically, in the case where the object detection unit 102 detects aplurality of objects from a video, the distinctiveness calculation unit106 (1206) may weight respective degrees of distinctiveness of thedetected objects, and sum up the weighted degrees thereby to obtain adegree of distinctiveness of a target frame.

Assume the following case for example. Three objects A, B, and C aredetected from a video, and respective degrees of distinctiveness of theobjects A, B, and C in a target frame are calculated as P_(A), P_(B),and P_(C). The interesting section identification device 100 (1200)includes an object specification unit for identifying the type of anobject detected by the object detection unit 102 with use of aclassifier for identifying a feature of each object. The interestingsection identification device 100 (1200) stores therein an objectclassification table for weighting objects where weighting values are inone-to-one correspondence with types of object such as person, animal,and building. The object classification table shows that the objects A,B, and C have weighting values W_(A), W_(B), and W_(C), respectively. Inthis case, the distinctiveness calculation unit 106 calculates a degreeof distinctiveness of the target frame asP_(A)×W_(A)+P_(B)×W_(B)+P_(C)×W_(C). In this way, the interestingsection identification device 100 (1200) may calculate a degree ofdistinctiveness in accordance with the type of an extracted object,thereby to extract an interesting section.

Further assume that an object A mainly appears in the video, in otherwords, the video is shot in which a main character is a personrepresented by the object A. In this case, it is desirable to identifyan interesting section such that a degree of distinctiveness of motionof the object A is mainly reflected to a degree of distinctiveness of atarget frame.

For this reason, the interesting section identification device 100(1200) includes the reception unit 1205 in the same way as in Embodiment2. Assume that the reception unit 1205 receives, from the user,designation of the object A that is a main object in the video. Theinteresting section identification device 100 (1200) sets the weightingvalue W_(A) to 1.5 and other weighting value to 0.5, such that theobject A is weighted higher. In this way, the interesting sectionidentification device 100 (1200) can identify an interesting sectionwhere a certain object is prioritized. Although the weighting valueW_(A) is set to 1.5 and other weighting value is set to 0.5 here, thesevalues are just examples. The object A only needs to be weighted higherthan other object.

Furthermore, the interesting section identification device 100 (1200)may perform weighting as shown below. In the above case, the interestingsection identification device 100 (1200) receives designation of theobject A from the user. Alternatively, the interesting sectionidentification device 100 (1200) may designate the object A in thefollowing manner.

The interesting section identification device 100 (1200) may include astorage medium storing therein a plurality of videos other than a videofrom which an interesting section is to be extracted, or have a functionof accessing an external storage medium (or a storage medium on thenetwork) storing therein such a plurality of videos.

The interesting section identification device 100 (1200) detects one ormore objects from the videos stored in the storage medium. The objectsare detected from an arbitrary frame that is extracted from each of thevideos. In other words, the objects may be detected from each of all theframes, or may be extracted from one or more frames selected at random.In order to judge whether the detected objects are the same object, theinteresting section identification device 100 (1200) clusters each ofthe objects based on a feature value of the object.

The interesting section identification device 100 (1200) identifies theobjects whose degrees of distinctiveness are clustered into the samecluster resulting from clustering as the same object.

The interesting section identification device 100 (1200) judges that anobject corresponding to a cluster having the highest number of nodes isan object (subject) that is important for the user. Note that the nodeis a feature value of the object corresponding to the cluster. Theobject A described above is identified in this way.

In the above case, the object A is identified as the objectcorresponding to the cluster having the highest number of nodes.Alternatively, a predetermined number of nodes may be set beforehand asa threshold value. When an object corresponding to a cluster whosenumber of nodes exceeds this threshold value, a degree ofdistinctiveness of the object may be weighted. This weighting only needsto be made such that the object corresponding to the cluster whosenumber of nodes exceeds the threshold value is weighted higher thanother object. Also, in the case where there are a plurality of objectscorresponding to clusters whose number of nodes exceeds the thresholdvalue, the corresponding objects are each weighted. In this case, auniform weighting value may be assigned to each of the objects, or adifferent weighting value in decreasing order of the number of nodes maybe multiplied for the object. Alternatively, with respect to each objectcorresponding to a cluster whose number of nodes exceeds the thresholdvalue, priority allocation may be made on a weighting value and theweighting value may be multiplied. Specifically, the total of weightingvalues may be predetermined, and with respect to each objectcorresponding to a cluster whose number of nodes exceeds the thresholdvalue, a weighting value is distributed to the object in accordance withthe number of nodes of the cluster.

Also, an object that is a main object in a video often appears in thecentral part in frames. For this reason, a weighting value may be set toa degree of distinctiveness of each detected object in accordance withjudgment results as to whether the object appears in the central part inthe frames.

For example, as the central part in the frame, a rectangle range is setwhose diagonal lines are (X1,Y1)-(X2, Y2) where X1, X2, Y1, and Y2satisfy X2>X1 and Y2>Y1, and are coordinate values of pixels in theframe. Judgment is made as to whether each object is inside the centralpart. A weighting value assigned for an object that is positioned insidethe central part is one, and a weighting value assigned to an objectthat is positioned outside the central part is zero. Here, the objectpositioned inside the central part only needs to be weighted higher thanthe object positioned outside the central part, and weighting values arenot limited to one and zero. In the case where an object is positionedon both the inside and the outside of the central part, the object isjudged to be positioned in one of the inside and the outside of thecentral part where whose area is occupied by the object larger than theother. The interesting section identification device 100 (1200) mayperform weighting in this way.

(5) In the above embodiments, the interesting section identificationunit 106 (1206) identifies, as an interesting section, a section havingthe predetermined length where the sum of respective degrees ofdistinctiveness of frames included therein is the highest among sectionseach having the predetermined length in a video. Alternatively, anyother method of identifying an interesting section may be employed aslong as a section where a motion is distinctive is identified.

For example, with respect to each two successive frames included in eachof sections in a video having the predetermined length, the interestingsection identification unit 106 (1206) may identify a point where thedifference in motion feature value between a frame and a subsequentframe exceeds a predetermined threshold value, and identify a sectionhaving the predetermined length where the number of such points is thehighest among the sections each having the predetermined length in thevideo.

Alternatively, the following method may be employed. The interestingsection identification unit 106 (1206) calculates the difference inmotion feature value between each two successive frames included in eachof sections in a video having the predetermined length, and identifies,as an interesting section, a section having the predetermined lengthwhere the sum of the differences is the highest among the sections eachhaving the predetermined length in the video.

(6) In the above embodiments, the number of interesting sections to beidentified from a video by the interesting section identification device100 (1200) is one. Alternatively, the number of interesting sections tobe identified from the video does not necessarily need to be one.

For example, the interesting section identification device 100 (1200)may identify, as an interesting section, each of a plurality of sectionseach having the predetermined length where the sum of degrees ofrespective distinctiveness of frames included therein exceeds apredetermined threshold value. In this case, instead of the sum of therespective degrees of distinctiveness of the frames, the average of therespective degrees of distinctiveness of the frames may be compared withthe predetermined threshold value. With this structure, the interestingsection identification device 100 (1200) can extract a plurality ofinteresting sections. In the case where two sections each having thepredetermined length that satisfy the above conditions overlap eachother, the interesting section identification device 100 (1200) mayidentify, as an interesting section, a section having a length thatexceeds the predetermined length and has the start point and the endpoint that are coincident with the start point of anterior one of thetwo overlapping sections and the end point of posterior one of the twooverlapping sections, respectively.

Also, in order to identify a plurality of interesting sections, theoutput unit 109 may output a digest movie resulting from piecing theidentified interesting sections together. Creation of the digest moviemay be performed by the interesting section extraction unit 108.

This improves convenience of the interesting section identificationdevice 100 (1200).

(7) In the above embodiments, the distinctiveness calculation unit 106(1206) calculates a degree of distinctiveness of each of all the framesof a video, and the interesting section identification unit 107 (1207)identifies an interesting section based on the respective degrees ofdistinctiveness of all of the frames. This is just one example.Alternatively, it is unnecessary to calculate the respective degrees ofdistinctiveness of all the frames as long as an interesting section isidentified with a certain degree of accuracy.

For example, the distinctiveness calculation unit 106 (1206) maycalculate a degree of distinctiveness of only each of odd-numberedframes or even-numbered frames of a video, or calculate a degree ofdistinctiveness of only frames selected at random among frames of thevideo. In other words, the distinctiveness calculation unit 106 (1206)may thin frames of the video for distinctiveness calculation. Theinteresting section identification unit 107 (1207) may identify aninteresting section only with use of frames whose respective degrees ofdistinctiveness are calculated.

With this structure, it is true that the accuracy of identifying aninteresting section deteriorates compared with the case where a degreeof distinctiveness is calculated with respect to each of all the framesof the video. However, it is possible to reduce the load of calculationprocessing performed by the interesting section identification device100 (1200), and shorten the time period required for identifying theinteresting section.

(8) In Embodiment 1, calculation is made as to how much a motion featurevalue of a target frame is distinctive from a motion feature value ofeach of frames included in a third section of a video. Alternatively,instead of the frames included in the third section, calculation may bemade as to how much the motion feature value of the target frame isdistinctive from a motion feature value of each of all the frames of thevideo excluding the target frame.

(9) In the above embodiments, the object chasing unit 103 chases anobject in a certain frame, and judges whether a position of the objectin the certain frame and a position of an object in a subsequent frameare close to each other (coordinate position of the object in theframe). If judging that the two positions are close to each other, theobject chasing unit 103 identifies the two objects as the same object.Alternatively, other method of identifying an object detected by theobject detection unit 102 may be employed. For example, the objectchasing unit 103 may chase an object in a certain frame, and calculate adegree of similarity between the object in the certain frame and anobject in a subsequent frame. The degree of similarity is for examplecalculated by calculating a degree of similarity in image feature valuebetween the objects with use of the Gabor filter or the like.

The objects between the frames can be identified as the same object bythis method.

(10) In the above embodiments, an area 302 determined by the areadetermination unit 104 includes an object (face) detected by the objectdetection unit 102 and a body that should be associated with thedetected object (face). Alternatively, the area 302 may be a range thatdoes not include the object (face) and the body. For example, in thecase where an object to be detected is a person's face, an area on whichevaluation of motion of the object is to be made may be an areaincluding only a face, namely, only the area 302 a in FIG. 3. In otherwords, the area 302 may be a range including only part of an area forthe object detected by the object detection unit 102 or a rangeincluding the entire area for the detected object such as described inthe above embodiments.

(11) In the above embodiments, the interesting section identificationunit 107 (1207) sums up respective degrees of distinctiveness of framesincluded in each section that is equivalent in length to the window 701,and identifies, as an interesting section, a section where the sum ofrespective degrees of distinctiveness of frames included therein is thehighest among sections that are each equivalent in length to the window701. Alternatively, instead of simply summing up respective degrees ofdistinctiveness of frames that fall within the window 701, othercalculation may be employed.

For example, before summing up the respective degrees of distinctivenessof frames that fall within the window 701, the interesting sectionidentification unit 107 (1207) may weight the degree of distinctivenessof a posterior frame among the frames that fall within the window 701.With this structure, the interesting section identification unit 107(1207) can easily identify an interesting section including parts wheremotion of an object varies largely more in the second half than in thefirst half.

(12) In the above embodiments, in order to calculate a motion featurevalue of an object based on an area on which evaluation of motion of theobject is to be made, the motion feature value calculation unit 105calculates an optical flow of each of pixels of the area, and obtainsinformation pieces indicating the optical flows as the motion featurevalue of the object.

Alternatively, the motion feature value calculation unit 105 maycalculate an optical flow of each of all the pixels of the entire image,and subtract the average of the calculated optical flows from theoptical flows of the pixels of the area. With this structure, it ispossible to reduce the influence of optical flows which result from thecase where though an object itself makes no motion, a video is pannedfrom side to side. Further alternatively, the motion feature valuecalculation unit 105 may calculate a difference in luminance between atarget frame including an area on which evaluation of motion of anobject is to be made and a frame anterior to the target frame, andcalculate a difference in luminance between the target frame and a frameposterior to the target frame, to obtain information indicating thecalculated differences as a motion feature value of the object. Yetalternatively, the motion feature value calculation unit 105 mayestimate a distance between the shooting device 120 and an object basedon the size of an area on which evaluation of motion of the object is tobe made in each frame, to obtain information indicating the calculateddistance as a motion feature value of the object.

Furthermore, a change amount in luminance may be employed as a motionfeature value instead of optical flows.

(13) In the above embodiments, the motion feature value in the area 302is represented by a set of optical flows of pixels arranged from theextreme left to the extreme right on the first line, pixels arrangedfrom the extreme left to the extreme right on the second line, . . . ,pixels arranged from the extreme left to the extreme right on the N_(th)line, . . . , pixels arranged from the extreme left to the extreme righton the lowest line. However, the arrangement order of optical flows isnot limited to this. Any other arrangement order may be employed as longas the arrangement order of pixels representing a motion feature valueof each object in each frame is common. For example, the set of opticalflows of pixels of the area 302 may be composed of pixels arranged fromthe extreme right to the extreme left on the first line, pixels arrangedfrom the extreme right to the extreme left on the second line, . . . ,pixels arranged from the extreme right to the extreme left on the lowestline. Alternatively, the set of optical flows of pixels of the area 302may be composed of pixels arranged from the lowest line to the highestline.

(14) In the above embodiments, the interesting section identificationunit 107 (1207) indexes the start point tag and the end point tag to thestart point and the end point of an interesting section identified in avideo, respectively. Alternatively, in the case where the interestingsection is set to have a fixed length, only one of the start point tagand the end point tag may be indexed. In this case, a point that isdistant from a point indicated by the one indexed tag by thepredetermined length for interesting section is the boundary of theinteresting section. Namely, in the case where only the start point tagis indexed, a point that is posterior to a point indicated by the startpoint tag by the predetermined length is the boundary of the interestingsection. Also, in the case where only the end point tag is indexed, apoint that is anterior to a point indicated by the start point tag bythe predetermined length is the boundary of the interesting section.

(15) Although no description is given in the above embodiments, theinteresting section identification device 100 (1200) may include astorage unit for storing therein a video acquired by the videoacquisition unit 101, a video to which information indicating the startpoint and the end point of an interesting section is indexed to a videoby the interesting section identification unit 107 (1207), a videoextracted by the interesting section extraction unit 108, and so on. Thestorage unit is, for example, embodied as an HDD (Hard Disc Drive), anSSD (Solid State Drive), a flash memory, or the like.

(16) In the above embodiments, the interesting section identificationdevice 100 (1200) acquires a video from the shooting device 120.Alternatively, a video may be acquired from other devices.

For example, the interesting section identification device 100 mayinclude therein a USB flash memory drive to acquire a video stored inthe USB flash memory drive. Alternatively, the interesting sectionidentification device 100 (1200) may have a network communicationfunction to download a video on the Internet.

(17) In the above embodiments, the output unit 109 outputs an extractedmovie of an interesting section to the display device 130. An outputdestination is not limited to the display device 130.

For example, the output unit 109 may output the movie of the interestingsection to a USB flash memory connected with the interesting sectionidentification device 100 (1200) for storage, or upload the movie of theextracted interesting section on the Internet.

Also, instead of only the extracted movie of the interesting section,the output unit 109 may output the entire video to which informationindicating the start point and the end point of the interesting sectionis indexed.

(18) In the above embodiments, the interesting section identificationdevice 100 (1200) extracts, as an interesting section, a section wherean object makes a large motion. However, there is a possibility that alarge motion is detected due to just a scene switching in a video.

In view of this, in the case where variation in motion feature value ina video from which an interesting section is to be extracted is higherby a predetermined threshold value or more, it is judged that sceneswitching occurs, and the following may be employed. The interestingsection identification device 100 (1200) judges that a scene switchingoccurs in the video, divides the video into two pieces with the boundaryof a point where the scene switching occurs, and extracts an interestingsection from each or one of the divided two pieces. Also in this case,instead of depending on whether variation in motion feature value ishigher by the predetermined threshold value or more, the interestingsection identification device 100 (1200) may judge whether the sceneswitching occurs depending on whether variation in degree ofdistinctiveness is higher by a predetermined threshold value or more.

(19) In Embodiment 2, the interesting section identification device 1200receives designation of a certain object included in a target frame, andcalculates a degree of distinctiveness of the certain object as a degreeof distinctiveness of the target frame.

However, there might be a case where designation of a certain object isnot received from the user. In this case, the interesting sectionidentification device 1200 selects a certain object. In other words,instead of the reception unit 1205 for receiving designation of acertain object from the user, the interesting section identificationdevice 1200 may include an object selection unit for selecting an objectin accordance with a predetermined algorithm.

Specifically, the following methods are adoptable.

The object detection unit 102 included in the interesting sectionidentification device 1200 detects one or more objects included in eachof frames of an input video. Then, the motion feature value calculationunit 105 calculates a motion feature value of each detected object.

The distinctiveness calculation unit 1206 calculates the average ofrespective motion feature values of all the objects included in eachframe. Then, with respect to each frame, the distinctiveness calculationunit 1206 calculates a distance (divergence) between a motion featurevalue (vector) of each object included in the frame and the calculatedaverage (vector). The distinctiveness calculation unit 1206 identifiesan object having the distance that is higher by a predeterminedthreshold value or more, as an object that makes a distinctive motion inthe frame, and selects the object as a certain object.

Then, the distinctiveness calculation unit 1206 may calculate a degreeof distinctiveness of the certain object by the method described inEmbodiment 2 to specific an interesting section. Note here that anymethod other than the method described in Embodiment 2 may be used foridentifying an interesting section. The distinctiveness calculation unit1206 may identify, as an interesting section, a section having thepredetermined length where the sum of respective distances in framesincluded therein calculated for selecting a certain object is thehighest among sections each having the predetermined length in a video.In this case, the section where the sum of the distances is the highestis identified as an interesting section. Alternatively, thedistinctiveness calculation unit 1206 may identify, as an interestingsection, a section including a frame where the distance is the highestand predetermined anterior and posterior frames to the frame.

Furthermore, in the same manner as by the method of identifying theobject A described in the modification example (4), the object selectionunit may select, as a certain object, an object that is frequentlyincluded in one or more videos other than a video from which aninteresting section is to be identified.

(20) The structures described in the above embodiments and modificationexamples may be combined with each other.

(21) A control program may be recorded in a recording medium ordistributed and made available via any type of communications channel.The control program is composed of program codes for causing aninteresting section identification device or a processor of arecording/playback device including such an interesting sectionidentification device such as a DVD player and a BD player andintegrated circuits connected with the processor to perform theoperations relating to communication, the processing of identifying aninteresting section described in the above embodiments (see FIG. 8 toFIG. 10, FIG. 13, and FIG. 14), and so on. The recording medium may bean IC card, a hard disk, an optical disc, a flexible disc, a ROM, or thelike. The control program distributed and made available is used bystorage in a memory or the like read by the processer such that theexecution of the control program by that processor also realizes each ofthe functions described in the above embodiments.

(22) Specification of an interesting section described in the aboveembodiments may be realized by requesting other computer and a devicesuch as a cloud server on the network to perform some of the processingperformed by the interesting section identification device 100 (1200)described in the above embodiments.

For example, the cloud server may detect an object from a video, insteadof the object detection unit 102 included in the interesting sectionidentification device 100 (1200). In this case, the cloud server assignsinformation of the detected object to the video, and the interestingsection identification device 100 (1200) receives the video to which theinformation is assigned. The interesting section identification device100 (1200) outputs, to the object chasing unit 103, the video to whichthe information of the detected object is assigned, so as to cause theobject chasing unit 103 to perform subsequent processing.

Alternatively, the interesting section identification device 100 (1200)may request other device to detect a feature value. For example, theobject detection unit 102 assigns, to a video from which an object isdetected, information of the object. The interesting sectionidentification device 100 (1200) transmits, to a cloud server or thelike, the video to which the information of the object is assigned. Thecloud server performs the functions of the object chasing unit 103, thearea determination unit 104, and the motion feature value calculationunit 105, with use of the video to which the information of the objectis assigned. The cloud server assigns, to the video, information of afeature value of each object detected from each frame, and transmits, tothe interesting section identification device 100 (1200), the video towhich the information of each of the feature values is assigned. Then,the interesting section identification device 100 (1200) performsprocessing subsequent to the distinctiveness calculation processing.

In this way, the interesting section identification device 100 (1200)may request an external device to perform part of the processingnecessary for identifying an interesting section. In this case, theinteresting section identification device 100 (1200) and the externaldevice transmit and receive data necessary for the processing to andfrom each other, as exemplified.

(23) The functional structural elements described in the aboveembodiments each may be embodied as a circuit for realizing itsfunctions, or may be embodied by one or more processors executing theprograms. Also, the interesting section identification device 100 (1200)described in the above embodiments may be structured as a package of anIC, an LSI, or other integrated circuit. This package is incorporatedinto various types of devices for use. As a result, the devices realizethe functions as described in the above embodiments.

The functional blocks are typically embodied as an LSI that is anintegrated circuit. Each of the functional blocks may be separatelyintegrated into a single chip, or integrated into a single chipincluding part or all of the functional blocks. The description isprovided on the basis of an LSI here. Alternatively, the name of theintegrated circuit may differ according to the degree of integration ofthe chips. Other integrated circuits include an IC, a system LSI, asuper LSI, and an ultra LSI. Furthermore, the method applied for formingintegrated circuits is not limited to the LSI, and the present inventionmay be realized on a dedicated circuit or a general purpose processor.For example, the present invention may be realized on an FPGA (FieldProgrammable Gate Array) programmable after manufacturing LSIs, or areconfigurable processor in which connection and settings of a circuitcell inside an LSI are reconfigurable after manufacturing LSIs.

<Supplement>

The following describes embodiments of the interesting sectionidentification device relating to the present invention and effects ofthe embodiments.

(a) An interesting section identification device relating to the presentinvention is an interesting section identification device thatidentifies an interesting section in a video that is estimated tointerest a user, the interesting section identification devicecomprising: an object detection unit configured to detect an objectincluded in a frame extracted from the video; a motion feature valueextraction unit configured to calculate a motion feature value of theobject in the frame; a distinctiveness calculation unit configured tocalculate a degree of distinctiveness in terms of motion of the objectwith respect to the frame, with use of the calculated motion featurevalue of the object in the frame; and an interesting sectionidentification unit configured to identify the interesting section inthe video, with use of the calculated degree of distinctiveness.

Also, an interesting section identification method relating to thepresent invention is an interesting section identification method foruse in an interesting section identification device that identifies aninteresting section in a video that is estimated to interest a user, theinteresting section identification method comprising: an objectdetecting step of detecting an object included in a frame extracted fromthe video; a motion feature value extracting step of calculating amotion feature value of the object in the frame; a distinctivenesscalculating step of calculating a degree of distinctiveness in terms ofmotion of the object with respect to the frame, with use of thecalculated motion feature value of the object in the frame; and aninteresting section identifying step of identifying the interestingsection in the video, with use of the calculated degree ofdistinctiveness.

Also, an interesting section identification program relating to thepresent invention is an interesting section identification program forcausing a computer to perform interesting section identificationprocessing of identifying an interesting section in a video that isestimated to interest a user, the interesting section identificationprogram comprising: an object detecting step of detecting an objectincluded in a frame extracted from the video; a motion feature valueextracting step of calculating a motion feature value of the object inthe frame; a distinctiveness calculating step of calculating a degree ofdistinctiveness in terms of motion of the object with respect to theframe, with use of the calculated motion feature value of the object inthe frame; and an interesting section identifying step of identifyingthe interesting section in the video, with use of the calculated degreeof distinctiveness.

With this structure, the interesting section identification device canidentify a frame where an object makes a distinctive motion in a video,thereby identifying an interesting section that keeps a user fromgetting bored. The identified interesting section may be used forcreating a digest movie, or may be displayed on a monitor for enablingthe user to recognize the details of the video.

(b) According to the interesting section identification device in Item(a), the object detection unit may detect the object from each of aplurality of frames extracted from the video, the motion feature valueextraction unit may calculate a motion feature value of the object ineach of the frames, and with respect to each of the frames that is atarget frame, the distinctiveness calculation unit may calculate adegree of distinctiveness of the motion feature value of the object inthe target frame from the respective motion feature values of the objectin all other of the frames.

With this structure, in the case where an object makes a distinctivemotion in the time axis direction, the interesting sectionidentification device can identify an interesting section including ascene where the object makes the distinctive motion.

(c) According to the interesting section identification device in Item(b), the distinctiveness calculation unit may calculate the degree ofdistinctiveness of the motion feature value of the object in the targetframe, based on an inner product of the motion feature value of theobject in the target frame and an average of the respective motionfeature values of the object in all other of the frames.

With this structure, the distinctiveness calculation unit calculates theinner product of the motion feature value of the target frame and theaverage of the respective motion feature values of all other of theframes. Accordingly, the calculated inner product is used as anappropriate index for distinctiveness calculation indicating how lessthe object in the target frame is similar to the object in the otherframes. The closer to zero the inner product is, the less the motion ofthe object in the target frame is similar to the object in the otherframes.

(d) According to the interesting section identification device in Item(c), the distinctiveness calculation unit may calculate the degree ofdistinctiveness of the motion feature value of the object in the targetframe, with use of respective motion feature values of the object inframes included in a first section including the target frame.

With this structure, the interesting section identification device cancalculate a degree of distinctiveness of each frame of the video fromother frames, and use the calculated degree of distinctiveness as anindex for identifying an interesting section. Also, a motion featurevalue is calculated not with respect to each of all the frames of theentire video but with respect to each of only frames included in thefirst section. This can reduce the load of calculation processingperformed by the interesting section identification device.

(e) According to the interesting section identification device in Item(d), the distinctiveness calculation unit may calculate the degree ofdistinctiveness of the motion feature value of the object in the targetframe with use of an average of respective motion feature values of theobject in frames included in a third section, the third sectionresulting from subtracting, from the first section, a second sectionthat includes the target frame and is shorter than the first section.

With this structure, the interesting section identification devicecalculates the degree of distinctiveness without using the respectivemotion feature values of the object in the frames included in the secondsection other than the target frame, thereby tolerating that aninteresting section includes a monotonous motion in the frames includedin the second section, because the user does not feel bored just byviewing such a boring motion for only several seconds.

(f) According to the interesting section identification device in Item(e), the distinctiveness calculation unit may determine a length of thesecond section based on a length of the first section.

The longer the interesting section is, the less the user feels boredeven if the interesting section includes some boring motions. With thisstructure, the interesting section identification device can set thesecond section having an appropriate length based on the length of thefirst section.

(g) According to the interesting section identification device in Item(e), the interesting section identification device may further comprisea setting unit configured to enable a user to set the second section.

With this structure, the interesting section identification device canidentify an interesting section in accordance with the user'spreference.

(h) According to the interesting section identification device in Item(d), the distinctiveness calculation unit may determine a length of thefirst section based on a length of the video.

With this structure, the interesting section identification device candetermine the first section for calculating the degree ofdistinctiveness based on the length of the video.

(i) According to the interesting section identification device in Item(d), the interesting section identification device may further comprisea setting unit configured to enable a user to set the first section.

With this structure, the interesting section identification device canidentify an interesting section in accordance with the user'spreference.

(j) According to the interesting section identification device in Item(a), the interesting section identification unit may identify, as theinteresting section, a section having a predetermined length where a sumof respective degrees of distinctiveness of the object in framesincluded therein is the highest among sections each having thepredetermined length in the video, with use of respective degrees ofdistinctiveness of the object in frames extracted from the video.

With this structure, the interesting section identification deviceidentify an interesting section where an object makes a distinctivemotion frequently.

(k) According to the interesting section identification device in Item(d), the distinctiveness calculation unit may calculate the degree ofdistinctiveness of the motion feature value of the object from a motionfeature value of other object included in the frame.

With this structure, the interesting section identification device canidentify, as an interesting section, a section including a frame wherean object makes a motion distinctive from other object in the frame.

(l) According to the interesting section identification device in Item(k), when a plurality of other objects are included in the frame, thedistinctiveness calculation unit may calculate the degree ofdistinctiveness of the motion feature value of the object from anaverage of respective motion feature values of the other objectsincluded in the frame.

With this structure, when the number of other objects included in theframe is plural, the interesting section identification device cancalculate a degree of distinctiveness for use in identifying aninteresting section. As a result, the interesting section identificationdevice can identify, as an interesting section, an NG scene where onlyone person irrelevantly makes a different motion while other personsmake the similar motion.

Therefore, the interesting section identification device can identify,as an interesting section, a section where motion is particularly activeand a degree of distinctiveness is high.

(m) According to the interesting section identification device in Item(a), the interesting section identification unit may index, to thevideo, information indicating a start point and end point of theidentified interesting section.

With this structure, the interesting section identification device canprovide a video including information of an interesting section. Sincethe information relating to the interesting section is indexed to thevideo, the interesting section identification device can use theinformation for designating a playback position (a skip destination) inthe video, for example.

(n) According to the interesting section identification device in Item(d), the interesting section identification device may further comprise:an interesting section extraction unit configured to extract, from thevideo, the interesting section identified by the interesting sectionidentification unit; and a digest creation unit configured to, when theinteresting section extraction unit extracts a plurality of interestingsections from the video, create a digest video by piecing the extractedinteresting sections together.

With this structure, the interesting section identification device cancreate a digest movie of the video. Accordingly, the user can easilyrecognize the details of the video by viewing the created digest movie.

(o) According to the interesting section identification device in Item(a), the interesting section identification device may further comprise:an object position detection unit configured to detect a position wherethe object is detected in each of frames extracted from the video; andan area determination unit configured to determine an area including theobject in each of the frames, the area being on which evaluation of themotion of the object is to be made, wherein the motion feature valueextraction unit may calculate the motion feature value of the object ineach of the frames, with use of an average of respective motion featurevalues of feature points in the area.

With this structure, the interesting section identification devicedetermines an area for an object in a frame, and uses motion of thedetermined area for detecting a degree of distinctiveness of the object.This makes it easy to detect the degree of distinctiveness of theobject, compared with the use of motion of the entire frame.

(p) According to the interesting section identification device in Item(a), when the object detection unit detects a plurality of objects fromthe frame, the distinctiveness calculation unit may calculate the degreeof distinctiveness by weighting respective motion feature values of theobjects in the frame.

With this structure, the interesting section identification deviceweights a motion feature value of an object in accordance with the typeof the object to calculate a degree of distinctiveness of the object.Accordingly, the interesting section identification device can identifyan interesting section that focuses on an object that attracts theuser's attention.

(q) According to the interesting section identification device in Item(p), the distinctiveness calculation unit may weight higher an objectthat is positioned within a certain range in the frame than an objectthat is positioned outside the certain range in the frame.

Generally, an object that is a main object in a video often appears inthe central part in frames. Accordingly, the interesting sectionidentification device for example sets a certain range to the centralpart in a frame to easily weight an object that is a main object in avideo, thereby identifying an interesting section.

(r) According to the interesting section identification device in Item(p), the interesting section identification device may further comprisea reception unit configured to receive designation of a certain objectdetected from the frame, wherein the distinctiveness calculation unitmay weight higher the certain object whose designation is received bythe reception unit than other object.

With this structure, the interesting section identification device canweight a designated object. In the case where, for example, the userdesignates an object that the user estimates to be a main object in avideo, the interesting section identification device can weight thisdesignated object to identify an interesting section where the objectmakes a distinctive motion. As a result, the interesting sectionidentification device identify an interesting section that interests theuser.

(s) According to the interesting section identification device in Item(r), the interesting section identification device may further comprisea storage unit configured to store therein one or more other videos,wherein the reception unit may receive, as the designation of thecertain object, designation of an object that is frequently included inthe other videos.

With this structure, even if receiving designation of no object from theuser, the interesting section identification device can select a certainobject, and weight the certain object. In the case where the interestingsection identification device stores therein a plurality of videos shotby the user, the videos each have a high possibility of including manyobjects that interest the user. Accordingly, the interesting sectionidentification device can identify that an object that is frequentlyincluded in a plurality of other videos is an object that interests theuser.

(t) According to the interesting section identification device in Item(a), the motion feature value may be represented by optical flows.

With this structure, optical flows (motion vectors), which are broadlyknown, are used for representing a motion feature value, therebyincreasing the versatility of the interesting section identificationdevice.

INDUSTRIAL APPLICABILITY

The interesting section identification device relating to the presentinvention is utilizable as a recording/playback device such as a DVDplayer and a BD player for creating a digest movie of a video.

REFERENCE SIGNS LIST

-   -   100 and 1200 interesting section identification device    -   101 video acquisition unit    -   102 object detection unit    -   103 object chasing unit    -   104 area determination unit    -   105 motion feature value calculation unit    -   106 and 1206 distinctiveness calculation unit    -   107 and 1207 interesting section identification unit    -   108 interesting section extraction unit    -   109 output unit    -   120 shooting device    -   130 display device    -   1205 reception unit

1. An interesting section identification device that identifies an interesting section in a video that is estimated to interest a user, the interesting section identification device comprising: an object detection unit configured to detect an object included in a frame extracted from the video; a motion feature value extraction unit configured to calculate a motion feature value of the object in the frame; a distinctiveness calculation unit configured to calculate a degree of distinctiveness in terms of motion of the object with respect to the frame, with use of the calculated motion feature value of the object in the frame; and an interesting section identification unit configured to identify the interesting section in the video, with use of the calculated degree of distinctiveness.
 2. The interesting section identification device of claim 1, wherein the object detection unit detects the object from each of a plurality of frames extracted from the video, the motion feature value extraction unit calculates a motion feature value of the object in each of the frames, and with respect to each of the frames that is a target frame, the distinctiveness calculation unit calculates a degree of distinctiveness of the motion feature value of the object in the target frame from the respective motion feature values of the object in all other of the frames.
 3. The interesting section identification device of claim 2, wherein the distinctiveness calculation unit calculates the degree of distinctiveness of the motion feature value of the object in the target frame, based on an inner product of the motion feature value of the object in the target frame and an average of the respective motion feature values of the object in all other of the frames.
 4. The interesting section identification device of claim 3, wherein the distinctiveness calculation unit calculates the degree of distinctiveness of the motion feature value of the object in the target frame, with use of respective motion feature values of the object in frames included in a first section including the target frame.
 5. The interesting section identification device of claim 4, wherein the distinctiveness calculation unit calculates the degree of distinctiveness of the motion feature value of the object in the target frame with use of an average of respective motion feature values of the object in frames included in a third section, the third section resulting from subtracting, from the first section, a second section that includes the target frame and is shorter than the first section.
 6. The interesting section identification device of claim 5, wherein the distinctiveness calculation unit determines a length of the second section based on a length of the first section.
 7. The interesting section identification device of claim 5, further comprising a setting unit configured to enable a user to set the second section.
 8. The interesting section identification device of claim 4, wherein the distinctiveness calculation unit determines a length of the first section based on a length of the video.
 9. The interesting section identification device of claim 4, further comprising a setting unit configured to enable a user to set the first section.
 10. The interesting section identification device of claim 1, wherein the interesting section identification unit identifies, as the interesting section, a section having a predetermined length where a sum of respective degrees of distinctiveness of the object in frames included therein is the highest among sections each having the predetermined length in the video, with use of respective degrees of distinctiveness of the object in frames extracted from the video.
 11. The interesting section identification device of claim 1, wherein the distinctiveness calculation unit calculates the degree of distinctiveness of the motion feature value of the object from a motion feature value of other object included in the frame.
 12. The interesting section identification device of claim 11, wherein when a plurality of other objects are included in the frame, the distinctiveness calculation unit calculates the degree of distinctiveness of the motion feature value of the object from an average of respective motion feature values of the other objects included in the frame.
 13. The interesting section identification device of claim 1, wherein the interesting section identification unit indexes, to the video, information indicating a start point and end point of the identified interesting section.
 14. The interesting section identification device of claim 1, further comprising: an interesting section extraction unit configured to extract, from the video, the interesting section identified by the interesting section identification unit; and a digest creation unit configured to, when the interesting section extraction unit extracts a plurality of interesting sections from the video, create a digest video by piecing the extracted interesting sections together.
 15. The interesting section identification device of claim 1, further comprising: an object position detection unit configured to detect a position where the object is detected in each of frames extracted from the video; and an area determination unit configured to determine an area including the object in each of the frames, the area being on which evaluation of the motion of the object is to be made, wherein the motion feature value extraction unit calculates the motion feature value of the object in each of the frames, with use of an average of respective motion feature values of feature points in the area.
 16. The interesting section identification device of claim 1, wherein when the object detection unit detects a plurality of objects from the frame, the distinctiveness calculation unit calculates the degree of distinctiveness by weighting respective motion feature values of the objects in the frame.
 17. The interesting section identification device of claim 16, wherein the distinctiveness calculation unit weights higher an object that is positioned within a certain range in the frame than an object that is positioned outside the certain range in the frame.
 18. The interesting section identification device of claim 16, further comprising a reception unit configured to receive designation of a certain object detected from the frame, wherein the distinctiveness calculation unit weights higher the certain object whose designation is received by the reception unit than other object.
 19. The interesting section identification device of claim 18, further comprising a storage unit configured to store therein one or more other videos, wherein the reception unit receives, as the designation of the certain object, designation of an object that is frequently included in the other videos.
 20. The interesting section identification device of claim 1, wherein the motion feature value is represented by optical flows.
 21. An interesting section identification method for use in an interesting section identification device that identifies an interesting section in a video that is estimated to interest a user, the interesting section identification method comprising: an object detecting step of detecting an object included in a frame extracted from the video; a motion feature value extracting step of calculating a motion feature value of the object in the frame; a distinctiveness calculating step of calculating a degree of distinctiveness in terms of motion of the object with respect to the frame, with use of the calculated motion feature value of the object in the frame; and an interesting section identifying step of identifying the interesting section in the video, with use of the calculated degree of distinctiveness.
 22. An interesting section identification program for causing a computer to perform interesting section identification processing of identifying an interesting section in a video that is estimated to interest a user, the interesting section identification program comprising: an object detecting step of detecting an object included in a frame extracted from the video; a motion feature value extracting step of calculating a motion feature value of the object in the frame; a distinctiveness calculating step of calculating a degree of distinctiveness in terms of motion of the object with respect to the frame, with use of the calculated motion feature value of the object in the frame; and an interesting section identifying step of identifying the interesting section in the video, with use of the calculated degree of distinctiveness. 